scholarly journals The Taste Sensors with Conductivity Measurement

2016 ◽  
Vol 10 (1) ◽  
pp. 37-43
Author(s):  
Hung-Wei Chen ◽  
Yi-Mu Lee ◽  
Shen-Li Chen
Keyword(s):  
Principal Component Analysis ◽  
Chemical Sensors ◽  
Conductive Polymer ◽  
Conductivity Measurement ◽  
Principal Component ◽  
Component Analysis ◽  
Liquid Solution ◽  
Electrical Signal ◽  
Voltage Signal ◽  
Sensors Array

There are many types of electrical signal that can be sensed from liquid solution. Among them conductivity signal is more suitable than voltage signal for the study of liquid. Thus, we choose conductivity as the target sense signal for the study of taste sensors. We developed a chemical sensors array with seven chemical sensors. There are two groups in the designed sensors. The first group is mainly made by the material of conductive polymer, the second group is made by the material of bio-mimic. A statistical method call “Principal Component Analysis” (PCA) is used to process the job of classified the tastes. The PCA results show the taste sensors array can distinguish four basic tastes in liquid. The experiment results show that four basic tastes are successful classified by principal component analysis. Six sport drinks are identified and classified in salt area. The results also show that quantities of salt sample can be analyzed by PCA method.

Probabilistic principal component analysis-based dimensionality reduction and optimization for arrhythmia classification using ECG signals

2019 ◽  
Vol 15 (1) ◽  
Author(s):  
Bhagyalakshmi Vishwanath ◽  
Ramchandra Vittal Pujeri ◽  
Geeta Devanagavi
Keyword(s):  
Principal Component Analysis ◽  
Heart Diseases ◽  
Principal Component ◽  
Component Analysis ◽  
Electrical Signal ◽  
Support Vector ◽  
Clinical Implementation ◽  
Ecg Signals ◽  
Probabilistic Principal Component Analysis

Abstract Electrocardiogram (ECG) is an electrical signal that contains data about the state and functions of the heart and can be used to diagnose various types of arrhythmias effectively. The modeling and simulation of ECG under different conditions are significant to understand the function of the cardiovascular system and in the diagnosis of heart diseases. Arrhythmia is a severe peril to the patient recovering from acute myocardial infarction. The reliable detection of arrhythmia is a challenge for a cardiovascular diagnostic system. As a result, a considerable amount of research has focused on the development of algorithms for the accurate diagnosis of arrhythmias. In this paper, a system for the classification of arrhythmia is developed by employing the probabilistic principal component analysis (PPCA) model. Initially, the cluster head is selected for the effective transmission of ECG signals of patients using the adaptive fractional artificial bee colony algorithm, and multipath routing for transmission is selected using the fractional bee BAT algorithm. Features such as wavelet features, Gabor transform, empirical mode decomposition, and linear predictive coding features are extracted from the ECG signal with high dimension (which are reduced using PPCA) and finally given to the proposed classifier called adaptive genetic-bat (AGB) support vector neural network (which is trained using the AGB algorithm) for arrhythmia detection. The experimentation of the proposed system is done based on evaluation metrics, such as the number of alive nodes, normalized network energy, goodput, and accuracy. The proposed method obtained a classification accuracy of 0.9865 and a goodput of 0.0590 and provides a better classification of arrhythmia. The experimental results show that the proposed system is useful for the classification of arrhythmias, with a reasonably high accuracy of 0.9865 and a goodput of 0.0590. The validation of the proposed system offers acceptable results for clinical implementation.

scholarly journals Graphene Foam Chemical Sensor System Based on Principal Component Analysis and Backpropagation Neural Network

2018 ◽  
Vol 2018 ◽  
pp. 1-8 ◽  
Author(s):  
Hongling Hua ◽  
Xiaohui Xie ◽  
Jinjin Sun ◽  
Ge Qin ◽  
Caiyan Tang ◽  
...  
Keyword(s):  
Neural Network ◽  
Principal Component Analysis ◽  
Chemical Sensors ◽  
Chemical Sensor ◽  
Detection System ◽  
Principal Component ◽  
Component Analysis ◽  
Backpropagation Neural Network ◽  
Graphene Foam ◽  
New Strategy

A kind of graphene foam chemical sensor (GFCS) system based on the principal component analysis (PCA) and backpropagation neural network (BPNN) was presented in this paper. Compared with conventional chemical sensors, the GFCS could discriminate various chemical molecules with selectivity without surface modification. The GFCS system consisted of an unmodified graphene foam chemical sensor, an electrical resistance time domain detection system (ERTDS), and a pattern recognition module. The GFCS has been validated via several chemical molecules discrimination including chloroform, acetone, and ether. The experimental results showed that the discrimination accuracy for each molecule exceeded 97% and a single measurement can be achieved in ten minutes. This work may have presented a new strategy for research and application for graphene chemical sensors.

A German version of the Intermittent Claudication Questionnaire (ICQ): cultural adaptation and validation

VASA ◽  
2012 ◽  
Vol 41 (5) ◽  
pp. 333-342 ◽  
Author(s):  
Kirchberger ◽  
Finger ◽  
Müller-Bühl
Keyword(s):  
Principal Component Analysis ◽  
Intermittent Claudication ◽  
Completion Time ◽  
Short Form ◽  
Principal Component ◽  
Component Analysis ◽  
German Version ◽  
Average Completion Time ◽  
Sf 36 ◽  
Related Quality

Background: The Intermittent Claudication Questionnaire (ICQ) is a short questionnaire for the assessment of health-related quality of life (HRQOL) in patients with intermittent claudication (IC). The objective of this study was to translate the ICQ into German and to investigate the psychometric properties of the German ICQ version in patients with IC. Patients and methods: The original English version was translated using a forward-backward method. The resulting German version was reviewed by the author of the original version and an experienced clinician. Finally, it was tested for clarity with 5 German patients with IC. A sample of 81 patients were administered the German ICQ. The sample consisted of 58.0 % male patients with a median age of 71 years and a median IC duration of 36 months. Test of feasibility included completeness of questionnaires, completion time, and ratings of clarity, length and relevance. Reliability was assessed through a retest in 13 patients at 14 days, and analysis of Cronbach’s alpha for internal consistency. Construct validity was investigated using principal component analysis. Concurrent validity was assessed by correlating the ICQ scores with the Short Form 36 Health Survey (SF-36) as well as clinical measures. Results: The ICQ was completely filled in by 73 subjects (90.1 %) with an average completion time of 6.3 minutes. Cronbach’s alpha coefficient reached 0.75. Intra-class correlation for test-retest reliability was r = 0.88. Principal component analysis resulted in a 3 factor solution. The first factor explained 51.5 of the total variation and all items had loadings of at least 0.65 on it. The ICQ was significantly associated with the SF-36 and treadmill-walking distances whereas no association was found for resting ABPI. Conclusions: The German version of the ICQ demonstrated good feasibility, satisfactory reliability and good validity. Responsiveness should be investigated in further validation studies.

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